Adaptive authentication systems typically perform authentication operations which involve the comparison of a recently received authentication request to conventional historical authentication requests issued by a known user such that a request with irregular behavior can be identified. For example, suppose that a bank customer normally logs into his account from London between 4 and 6 PM. Suppose further that the bank receives a series of login attempts to that account between the hours of 2 and 4 AM from a location in Texas. In such an example, authentication will more than likely be considered unsuccessful and some follow-on or remedial activity will usually takes place, e.g., a retry of authentication, step-up authentication, outputting an alert, and so on.
As will be known in the art, these type of adaptive authentication systems can employ a machine learning process which facilitates the building and updating of a model used to assess the risk of the authentication requests received at the authentication system. Typically, the updating of these models is performed by employing explicit feedback from analysts. Such explicit feedback can consist of an assessment of particular requests as either being fraudulent or non-fraudulent based on the results of a manual investigation. The machine learning module can take in the results of the manual investigation and update the model to account for differences between predictions of the model and results of the manual investigation.
However, the above suffers in that the process of tuning the model based on explicit feedback is limited. Along these lines, the cost of carrying out the investigations that generate the results used as input into a machine learning process may be expensive. With a finite budget for investigations, the amount of input for the machine learning process is limited.
There is therefore a need for another approach which can assist in assessing authentication requests.